It is difficult to reconcile our general architecture for the
linearised diff representation with the processing of recursive,
tree-like data structures. The natural and most clean way to
deal with trees is to use recursion, i.e. the processor stack.
But in our case, this means we'd have to peek into the next
token of the language and then forward the diff iterator
into a recursive call on the nested scope. Essentially, this
breaks the separation between receiving a token sequence and
interpretation for a concrete target data structure.
For this reason, it is preferrable to make the stack an
internal state of the concrete interpreter. The downside of
this approach is the quite confusing data storage management;
we try to make the role of the storage elements a bit more
clear through descriptive accessor functions.
implement the list handling primitives analogous to the
implementation of list-diff-applicator -- just again with
the additional twist to keep the attribute and child scopes
separated.
...so now the stage is set. We can reimplement
the handling of the list diff cases here in the context
of tree diff application. The additional twist of course
being the distinction between attribute and child scope
each language token of our "linearised diff representation"
carries a payload data element, which typically is the piece
of data to be altered (added, mutated, etc).
Basically, these elements have value semantics and are
"sent over wire", and thus it seems natural when the
language interpreter functions accept that piece of payload
by-value. But since we're now sending GenNode elements as
parameter data in our diff, which typically are of the
size of 10 data elements (640 bit on a 64bit machine),
it seems more resonable to pass these argument elements
by const& through the interpreter function. This still
means we can (and will indeed) copy the mutated data
values when applying the diff, but we're able to
relay the data more efficiently to the point where
it's consumed.
this boils down to the two alternatives
- manipulate the target data structure
- build an altered copy
since our goal is to handle large tree structures efficiently,
the decision was cast in favour of data manipulation
so basically it's time to explicate the way
our diff language will actually be written.
Similar to the list diff case, it's a linear sequence
of verb tokens, but in this case, the payload value
in each token is a GenNode. This is the very reason
why GenNode was conceived as value object with an
opaque DataCap payload
